CN113257391B

CN113257391B - Course of disease management system of skin disease

Info

Publication number: CN113257391B
Application number: CN202110614678.9A
Authority: CN
Inventors: 张伟; 张靖; 崔涛; 王胜
Original assignee: Hangzhou Yongliu Technology Co ltd
Current assignee: Hangzhou Yongliu Technology Co ltd
Priority date: 2021-06-02
Filing date: 2021-06-02
Publication date: 2021-11-30
Anticipated expiration: 2041-06-02
Also published as: CN113257391A

Abstract

The invention relates to a disease course management system for skin diseases, which comprises: the system comprises a data acquisition module, a data storage module, a data analysis module and a data display module; the data acquisition module is used for performing real-time framing and displaying an acquisition auxiliary line during the real-time framing; collecting skin disease information; the data storage module is used for storing the skin disease information acquired by the acquisition module; the data analysis module is used for analyzing the real-time framing of the data acquisition module; analyzing the information stored by the data storage module; and the data display module is used for displaying the analysis result of the stored information obtained by the data analysis module. The skin lesion data acquired by the system are acquired in a real-time view mode, the mode is suitable for any acquisition equipment, the application scene of course management is expanded, and the course management aiming at a universal environment is realized.

Description

Course of disease management system of skin disease

Technical Field

The invention relates to the technical field of remote medical treatment, in particular to a course management system for skin diseases.

Background

With the pace of human progress, the environments in which people rely on to live are also constantly changing. The atmospheric pollution is increasingly serious, so that the incidence rate of skin diseases is continuously increased, and the pathogenic factors of the skin diseases are also continuously upgraded. The dermatosis is a common disease and a frequently encountered disease in medicine, and has the characteristics of wide disease range, multiple disease types, long treatment time and the like.

In the existing pathological diagnosis system, a doctor firstly acquires index information required to be detected by a patient through various medical means, then transmits the acquired data to a data acquisition module, a data analysis module screens and analyzes the acquired data and then stores the data in a data storage module, and a diagnosis decision module makes a preliminary diagnosis result according to the acquired data; the medical staff can see the preliminary diagnosis result on the browser through the server, and further judge according to the preliminary diagnosis result of the diagnosis decision module through the remote operation module to make a final diagnosis conclusion. The medical staff can also print the diagnosis result as required.

The existing system index information is acquired through a medical means, the medical means requires that a patient can only be used in a hospital, and a common patient cannot acquire data through the medical means in a general environment (such as at home), so that the existing scheme is limited in application scene.

Disclosure of Invention

In view of the above disadvantages and shortcomings of the prior art, the present invention provides a system for managing the course of skin diseases.

In order to achieve the purpose, the invention adopts the main technical scheme that:

a system for managing the course of a skin disorder, the system comprising: the data acquisition module, the data storage module, the data analysis module and the data display module;

the data acquisition module is used for performing real-time view finding, acquiring skin damage data and displaying an acquisition auxiliary line during the real-time view finding; the device is also used for collecting other skin disease information; the other skin disease information comprises patient information, doctor information, binding relationship between the patient and the doctor, case of the patient, follow-up information of the patient and re-diagnosis information of the patient; the acquisition auxiliary line is obtained by the data analysis module according to an acquisition standard;

the data storage module is used for storing the skin disease information acquired by the data acquisition module;

the data analysis module is used for analyzing the real-time framing of the data acquisition module; analyzing the information stored by the data storage module;

the data display module is used for displaying the analysis result of the data analysis module on the stored information;

wherein, the data analysis module analyzes the data acquisition module live view, including:

identifying the real-time framing to determine whether a skin damage area exists;

if no skin damage area exists, obtaining an analysis result which does not accord with the acquisition standard;

if the skin damage area exists, determining a boundary line of the skin damage area;

if no boundary line of the skin damage area exists, obtaining an analysis result which does not accord with the acquisition standard;

if the boundary line of the skin damage area exists, determining whether the boundary line is a closed curve;

if the boundary line is not a closed curve, obtaining an analysis result which does not accord with the acquisition standard;

and if the boundary line is a closed curve, analyzing according to the relation between the acquisition auxiliary line and the skin damage area.

The invention has the beneficial effects that: the remote auxiliary medical treatment is realized through the data acquisition module, the data storage module, the data analysis module and the data display module in the disease course management system.

The skin lesion data acquired by the system are acquired in a real-time view mode, the mode is suitable for any acquisition equipment, the application scene of disease course management is expanded, and the disease course management aiming at a general environment (such as home) is realized. In addition, the disease course management system can realize the collection and auxiliary analysis of the skin damage data of the user at any time and any place, improve the use experience of the user and ensure the inquiry effect of the user.

Drawings

Fig. 1 is a schematic structural diagram of a system for managing a disease course of a skin disease according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an auxiliary line for acquisition according to an embodiment of the present invention;

FIG. 3 is a schematic view of a continuum of regions according to an embodiment of the invention;

fig. 4 is a schematic view of another continuous area provided in an embodiment of the present invention.

Detailed Description

For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.

Referring to fig. 1, the disease course management system for skin diseases provided in this embodiment includes: the device comprises a data acquisition module, a data storage module, a data analysis module and a data display module.

1. Data acquisition module

The data acquisition module is used for performing real-time view finding, acquiring skin damage data and displaying an acquisition auxiliary line during the real-time view finding; the device is also used for collecting other skin disease information; other skin disease information includes patient information, doctor information, binding relationship between patient and doctor, patient's case, patient's follow-up information and patient's follow-up information; the acquisition auxiliary line is obtained by the data analysis module according to the acquisition standard.

2. Data storage module

And the data storage module is used for storing the skin disease information acquired by the acquisition module.

3. Data analysis module

And the data analysis module is used for analyzing the real-time framing of the data acquisition module and analyzing the information stored by the data storage module.

Wherein, the data analysis module carries out the analytic process to data acquisition module live-view: identifying the real-time framing to determine whether a skin damage area exists; if no skin damage area exists, obtaining an analysis result which does not accord with the acquisition standard; if the skin damage area exists, determining a boundary line of the skin damage area; if no boundary line of the skin damage area exists, obtaining an analysis result which does not accord with the acquisition standard; if the boundary line of the skin damage area exists, determining whether the boundary line is a closed curve; if the boundary line is not a closed curve, obtaining an analysis result which does not accord with the acquisition standard; and if the boundary line is a closed curve, analyzing according to the relation between the acquisition auxiliary line and the skin damage area.

The data analysis module can also be used for acquiring a diagnosis standard of skin diseases according to the skin damage data, generating a form according to the diagnosis standard, acquiring feedback information based on the form, and acquiring an auxiliary inquiry result according to a preset decision tree and the feedback information.

In addition, the data analysis module can also be used for determining the current acquisition object and the acquisition standard according to the diagnosis standard and generating the acquisition instruction according to the current acquisition object and the acquisition standard. And starting the data acquisition module, analyzing the real-time framing of the data acquisition module, and controlling the current image of the data acquisition module to be acquired to obtain the skin damage data if the analysis result meets the acquisition standard. In this embodiment, the acquired image is skin damage data.

Correspondingly, the data display module is also used for displaying the acquisition instruction.

In addition, if the data acquisition module displays the acquisition auxiliary line, when the data analysis module analyzes the live view of the data acquisition module, 1) the live view is identified to determine whether the skin damage area exists. 2) And if the skin damage area does not exist, obtaining an analysis result which does not accord with the acquisition standard. 3) And if the skin damage area exists, determining the boundary line of the skin damage area. 4) And if the boundary line of the skin damage area does not exist, obtaining an analysis result which does not accord with the acquisition standard. 5) And if the boundary line of the skin damage area exists, determining whether the boundary line is a closed curve. 6) And if the boundary line is not a closed curve, obtaining an analysis result which does not accord with the acquisition standard. 7) And if the boundary line is a closed curve, obtaining an analysis result according to the relation between the acquisition auxiliary line and the skin damage area.

If the acquisition auxiliary line is a closed curve, the implementation process of obtaining the analysis result according to the relationship between the acquisition auxiliary line and the skin lesion area in 7) is as follows:

A. and forming a standard set by the pixel points in the region surrounded by the acquisition auxiliary lines.

B. And forming a skin damage set by the pixel points related to the skin element area.

C. And obtaining the intersection of the first set which is the standard set and the skin damage set.

D. If the elements in the element/standard set in the first set are <0.8 x (1+ total pixels of the image collected by the element/data collection module in the standard set), an analysis result which does not meet the collection standard is obtained.

E. If the element in the first set/the element in the standard set > =0.8 = (1+ the element in the standard set/the total pixels of the image acquired by the data acquisition module), the second set is the skin loss set-standard set, and if the second set is an empty set, the analysis result meeting the acquisition standard is obtained. And if the second set is not an empty set, obtaining an analysis result according to the second set.

For example, e.1 determines the number of contiguous regions to which the elements in the second set correspond, and the number of elements in the second set included in each contiguous region. And E.2, if the number of elements included in the maximum continuous area/the total number of elements in the first set is greater than 0.5, or the number of the continuous areas is greater than 4, obtaining an analysis result which does not meet the acquisition standard. Wherein the largest continuous area is the continuous area including the largest number of elements. And E.3, if the number of elements included in the maximum continuous area/the total number of elements in the first set is < =0.5 and the number of continuous areas is < =4, obtaining an analysis result meeting the acquisition standard.

In addition, the process of generating the form by the data analysis module according to the diagnosis standard is as follows: determining an acquisition index according to a diagnosis standard; the acquisition index comprises a serial number; the initial value of the serial number is null; determining a logical relation among all the acquisition indexes; the logical relation between any two indexes is used for describing the sequence of the appearance of any two indexes in the form; determining the position sequence of each acquisition index in the form according to the sequence number and the logical relation; and generating a form for each acquisition index according to the position sequence.

When the data analysis module determines the position sequence of each acquisition index in the form according to the sequence number and the logical relationship, the following implementation scheme is adopted: step 1, forming a set A of all acquisition indexes and generating an empty set B; step 2, arbitrarily selecting one acquisition index from the set A, using the acquisition index as a current processing index, and marking the serial number of the current processing index as 0; step 3, deleting the current processing index from the set A, and adding the current processing index into the set B; step 4, determining whether a first indicator exists, wherein the first indicator is an element in the set A, and the logical relationship between the first indicator and the current processing indicator is as follows: the first index appears in the form before the current processing index; step 5, if the first index exists, determining a second index corresponding to the first index, wherein the second index is an element in the set B, and the logical relationship between the second index and the first index is as follows: the second indicator appears in the form after the first indicator; marking the serial number of the first index as X-1, wherein X is the minimum serial number in the second index; deleting the first index from the set A, and adding the first index into the set B; step 6, determining whether a third index exists, wherein the third index is an element in the set A, and the logical relationship between the third index and the current processing index is as follows: the third index appears in the form after the current processing index; step 7, if the third index exists, determining a fourth index corresponding to the third index, where the fourth index is an element in the set B, and a logical relationship between the fourth index and the third index is: the fourth index appears in the form before the third index; marking the serial number of the third index as Y +1, wherein Y is the largest serial number in the fourth index; deleting the third index from the set A, and adding the third index into the set B; step 8, if the set A is not an empty set, taking any acquisition index from the set A as a current processing index, and repeatedly executing the steps 3 to 7 until the set A is an empty set, or each element in the set A does not have a first index and a third index; and 9, determining the position sequence of each acquisition index in the form according to the sequence number of the acquisition index.

If the set a is an empty set, step 9 specifically includes: determining the minimum value min of the sequence numbers in all the elements of the set B; determining whether elements with the same sequence number exist in the set B; if the elements with the same serial number do not exist in the set B, the position of each acquisition index in the form is the serial number-min +1 of each acquisition index; if the elements with the same sequence number exist in the set B, determining the original value of each acquisition index as the sequence number-min +1 of each acquisition index; sequencing all the acquisition indexes in the order of small to large original values; from the first element of the sequence, whether the same original value exists is confirmed in sequence; if the same original value exists, determining the number n of the same original value, adjusting the same original value according to n, and updating the original value of the index collected after the same original value to be the original value + n-1 before updating; starting from the original value after the same original value, re-executing the steps of sequentially confirming whether the same original value exists or not, if so, determining the number n of the same original value, adjusting the same original value according to n, and updating the original value of the index collected after the same original value to the original value before updating + n-1 until all the original values are confirmed; and determining the current original value of each acquisition index as the position of each acquisition index in the form.

If the set a is not an empty set, step 9 specifically includes: determining the number e1 of elements in the set A, and determining the total number e2 of the collection indexes; randomly selecting e1 numbers from continuous positive integers from 1 to e2, and randomly allocating the numbers to each element in the set A as the positions of the corresponding acquisition indexes in the form; determining the minimum value min of the sequence numbers in all the elements of the set B; determining whether elements with the same sequence number exist in the set B; if the elements with the same sequence number do not exist in the set B, determining a first value of the acquisition index corresponding to each element in the set B as a sequence number-min +1 of the acquisition index; sorting the corresponding acquisition indexes from small to large according to the first value; sequentially selecting one acquisition index from the first ordered acquisition index, and if the first value of the selected acquisition index is not one of the randomly selected e1 numbers, taking the first value as the position of the selected acquisition index in the form; if the first value of the selected acquisition index is one of the randomly selected e1 numbers, taking the first value +1 as the position of the selected acquisition index in the form, and updating the first values of all the acquisition indexes after the selected acquisition index to the first value +1 before updating; if the elements with the same sequence number exist in the set B, determining the second value of each acquisition index as the sequence number-min +1 of each acquisition index; sequencing the acquisition indexes in a sequence from small to large according to a second value; sequentially selecting one acquisition index from the second ordered acquisition index, and if the second value of the selected acquisition index is unique, taking the second value as the position of the selected acquisition index in the form when the second value of the selected acquisition index is not one of the randomly selected e1 numbers; when the second value of the selected acquisition index is one of the randomly selected e1, taking the second value +1 as the position of the selected acquisition index in the form, and updating the second values of all the acquisition indexes after the selected acquisition index to the second value +1 before updating; s902, if the second value of the selected acquisition index is not unique, marking an acquisition index before the selected acquisition index as an initial index, determining the same second value number n, adjusting the same second value according to n, and updating the second value of the acquisition index after the same second value to be the current second value + n-1; and sequencing the acquisition indexes in the sequence from small to large according to the updated second value, sequentially selecting one acquisition index from the next acquisition index of the initial index in the sequence, and repeatedly executing the step S901 and the step S902 until all the acquisition indexes have the positions of the acquisition indexes in the form.

The implementation of adjusting the same original value according to n is: counting in the historical forms, wherein the number m1 of the forms of all indexes to be adjusted and the sequence of the indexes to be adjusted in each form appear simultaneously; the indexes to be adjusted are acquisition indexes corresponding to the same original values; counting the number m2i of the forms with the index i to be adjusted in the historical form for any index i to be adjusted, and calculating an adjustment coefficient wi = (m1/m2i) × SQRT (S/m1) + Z of the index i to be adjusted; wherein S = POWER [ (a1i-m1)/2] + POWER [ (a2i-m1)/2] + … + POWER [ (ani-m1)/2 ]; SQRT () is an open root function, POWER () is a square function, a1i is the number of forms in which all indexes to be adjusted appear simultaneously, and the index i to be adjusted precedes all other indexes to be adjusted; a2i is the number of the forms in which the index i to be adjusted is located at the second place of all other indexes to be adjusted in all the forms in which the indexes to be adjusted appear at the same time; ani is the number of the forms in which all indexes to be adjusted appear simultaneously, the indexes to be adjusted i finally appear in all other indexes to be adjusted, and Z is a random decimal; sorting the indexes to be adjusted according to the adjustment coefficients from large to small, and determining a sorting label b of each adjustment index, wherein the sorting label of the index to be adjusted arranged at the first position is 0; and adjusting the original value of each index to be adjusted to be the original value + b before adjustment.

4. Data display module

And the data display module is used for displaying the analysis result of the stored information obtained by the data analysis module. In addition, the data display module can also be used for displaying a form and assisting the inquiry result. Furthermore, the data display module can also be used for displaying the acquisition instruction.

The following is a detailed description of the workflow of the disease course management system in practical application:

and S101, data acquisition.

The data acquisition module acquires skin lesion data and other skin disease information. The other skin condition information includes, but is not limited to, patient information, doctor information, binding relationships between patients and doctors, patient cases, patient follow-up information, and patient follow-up information.

In addition, the data acquisition module can display an acquisition auxiliary line in the process of carrying out real-time framing and data acquisition. As shown in fig. 2.

Wherein, the acquisition auxiliary line is obtained by the data analysis module according to the acquisition standard. The acquisition auxiliary line is a closed curve, such as a face contour line in fig. 2, and fig. 2 takes the face contour as an example, during actual acquisition, the acquisition auxiliary line may be circular, rectangular, or irregular, and the shape enclosed by the acquisition auxiliary line is not limited in this embodiment.

When the acquisition auxiliary line is displayed, whether the data acquisition module performs data acquisition or not is controlled by the data analysis module. The specific control flow is as follows:

s201, the data analysis module determines a current acquisition object and an acquisition standard according to the diagnosis standard, and generates an acquisition instruction according to the current acquisition object and the acquisition standard.

And S202, the data display module displays the acquisition description.

And S203, the data analysis module starts the data acquisition module and analyzes the real-time framing of the data acquisition module.

The analytical procedure was as follows:

1) and the data analysis module identifies the real-time framing and determines whether a skin damage area exists. If there is no skin damage region, execution 2), if there is a skin damage region, execution 3) -7).

The step can determine whether the skin damage area exists through the existing image recognition technology, and the existing scheme is adopted, so that the steps are not repeated.

2) And the data analysis module obtains an analysis result which does not accord with the acquisition standard.

If there is no skin damage area, then no acquisition is needed, so the data analysis module obtains an analysis result that does not meet the acquisition criteria.

3) The data analysis module determines a boundary line for the skin lesion area. If there is no boundary line of the damaged area, execution is 4), and if there is a boundary line of the damaged area, execution is 5) -7).

For the boundary line, a dividing line between the skin damage area and the normal skin can be obtained through image recognition, and the line is the boundary line. The scheme of image recognition may adopt an existing scheme, which is not described herein.

4) And the data analysis module obtains an analysis result which does not accord with the acquisition standard.

If no boundary line of the skin damage area exists, it is indicated that the image in the viewing range of the current data acquisition module does not include all skin damage images, but only a sub-image of the skin damage part (for example, the data acquisition module is too close to the skin damage part), and at this time, the acquisition has no meaning, so that the data analysis module obtains an analysis result which does not meet the acquisition standard.

5) The data analysis module determines whether the boundary line is a closed curve. If the boundary line is not a closed curve, 6) is executed, and if the boundary line is a closed curve, 7) is executed.

6) And the data analysis module obtains an analysis result which does not accord with the acquisition standard.

If the boundary line is not closed, it is indicated that part of the skin damage area does not appear in the view finding range of the current data acquisition module, and the data acquisition module needs to be adjusted, so that the data analysis module obtains an analysis result which does not meet the acquisition standard.

To this end, it is only guaranteed that the viewing range of the data acquisition module includes a complete skin damage area, but it does not mean that the acquisition auxiliary line includes a complete skin damage area, and therefore, the final judgment is made by 7).

7) And the data analysis module obtains an analysis result according to the relation between the acquisition auxiliary line and the skin damage area.

Because the acquisition auxiliary line is a closed curve, the implementation process of obtaining the analysis result according to the relationship between the acquisition auxiliary line and the skin lesion region in 7) is as follows:

A. and the data analysis module forms a standard set by the pixel points in the region surrounded by the acquisition auxiliary lines.

B. And the data analysis module forms a skin damage set by the pixel points related to the skin element area.

C. And the data analysis module obtains the intersection of the first set which is the standard set and the skin damage set.

The standard set is all the pixel points in the area enclosed by the acquisition auxiliary lines, the skin loss set is only all the pixel points corresponding to the skin loss area, and then the elements in the first set are the pixel points which are the pixel points in the skin loss area in the area enclosed by the acquisition auxiliary lines.

D. If the elements in the element/standard set in the first set are <0.8 x (1+ total pixels of the image collected by the element/data collection module in the standard set), the data analysis module obtains an analysis result which does not meet the collection standard.

If the number of pixels in the skin loss region in the region surrounded by the acquisition auxiliary lines is small (less than 0.8 (1+ total pixels of the image acquired by the element/data acquisition module in the standard set)), the skin loss region in the region surrounded by the acquisition auxiliary lines is small, the acquisition standard is not met, and the data analysis module obtains an analysis result which is not met with the acquisition standard.

E. If the element in the first set/the element in the standard set > =0.8 = (1+ the total pixels of the image collected by the element/data collection module in the standard set), the data analysis module obtains that the second set is the skin damage set-standard set.

The elements in the second set are pixels, which are pixels in the skin damage region but not pixels in the region surrounded by the acquisition auxiliary lines. That is, the pixel points in the second set are the pixel points related to the skin damage region outside the region enclosed by the acquisition auxiliary lines. The case of a skin lesion area outside the area enclosed by the acquisition aid lines is described.

F. And if the second set is an empty set, the data analysis module obtains an analysis result meeting the acquisition standard.

If the second set is an empty set, it indicates that there is no skin damage region outside the region surrounded by the acquisition auxiliary lines, and at this time, the area of the skin damage region in the region surrounded by the acquisition auxiliary lines reaches the standard (= 0.8 × (1+ total pixels of the image acquired by the element/data acquisition module in the standard set)), and acquisition can be performed, so that the data analysis module obtains an analysis result meeting the acquisition standard.

G. And if the second set is not an empty set, the data analysis module obtains an analysis result according to the second set.

If the second set is not an empty set, the skin damage area is indicated to exist outside the area enclosed by the acquisition auxiliary lines. The data analysis module determines the number of the continuous regions corresponding to the elements in the second set and the number of the elements in the second set included in each continuous region. If the number of elements included in the maximum continuous region/the total number of elements in the first set is greater than 0.5, or the number of the continuous regions is greater than 4, the data analysis module obtains an analysis result which does not meet the acquisition standard. If the number of elements included in the maximum continuous region/the total number of elements in the first set < =0.5 and the number of continuous regions < =4, the data analysis module obtains an analysis result meeting the acquisition standard.

Wherein the largest continuous area is the continuous area including the largest number of elements.

That is, the data analysis module determines the number of continuous regions corresponding to the elements in the second set, and determines how many skin damage regions are located outside the region surrounded by the acquisition auxiliary line according to the number of continuous regions, as shown in fig. 3, where the number of continuous regions is 3. As shown in fig. 4, the number of continuous regions at this time is 1.

Reference numerals

1, 2, 3 in fig. 3 are the number of continuous regions.

The determination method of the continuous area can be determined by collecting the relation between the auxiliary line and the skin damage area, and the existing implementation scheme is adopted, and is not limited here.

Meanwhile, the data analysis module determines the number of elements in the second set included in each continuous region, and then sets the continuous region with the largest number as the maximum continuous region.

Then, the data analysis module is used for analyzing the number of the continuous areas and the maximum number of the elements included in the continuous areas according to the relation result of the total number of the elements in the first set.

Although the skin damage area exceeds the area surrounded by the acquisition auxiliary line in the step, the skin damage area still can be acquired if the area is not large, the acquisition quality is ensured, an automatic acquisition scheme is provided, the difficulty of acquiring the skin damage area data by a user is reduced, and the user automatic acquisition is realized.

And S204, if the analysis result meets the acquisition standard, controlling the data acquisition module to acquire the current image to obtain the skin damage data. That is, the acquired image is lesion data.

And S102, storing data.

The data storage module stores the skin disease information acquired by the acquisition module. In addition, after the data acquisition module acquires data and before the data storage module stores the skin disease information acquired by the acquisition module, the data analysis module can label the information acquired by the data acquisition module. Such as marking the human body part corresponding to the skin damage data.

The labeling process is as follows:

s301, the data analysis module obtains a human body part corresponding to the skin damage data.

The human body part includes, but is not limited to, one of the following: head and neck parts, trunk parts, upper limb parts and lower limb parts.

For the head and neck type parts, the trunk type parts, the upper limb type parts, and the lower limb type parts, reference is made to the contents of the respective parts exemplified in the above embodiments.

S302, the data analysis module determines the user attribute corresponding to the skin damage data.

The implementation process of the step is as follows:

s302-1, obtaining historical skin damage data.

The historical skin damage data is skin damage data in the historical skin disease information, and for convenience of description, the historical skin damage data is simply referred to as the historical skin damage data in the embodiment.

In addition, in this step, not all the historical skin damage data are obtained, but skin damage data satisfying a preset relationship is obtained, where the preset relationship is: for any one of the acquired historical skin damage data D1, the human body part to which D1 belongs is the same as the human body part labeled in S301, and at the same time, 0.8< skin damage area in D1/skin damage area in skin damage data <1.2, and 0.8< (maximum value of gray levels of all pixels related to skin damage area in D1-minimum value of gray levels of all pixels related to skin damage area in D1)/(minimum value of gray levels of all pixels related to skin damage area in skin damage data-minimum value of gray levels of all pixels related to skin damage area in skin damage data) <1.2 are satisfied.

The lesion data here is the lesion data acquired in S101.

That is, if a historical damage data D2, whose labeled body part is the same as the body part labeled in S102 (for example, both right feet), and the value of the damage area/the damage area in the damage data is between 0.8 (excluding 0.8) and 1.2 (excluding 1.2), and the value of (the maximum value of the gray levels of all the pixels to which the damage area relates-the minimum value of the gray levels of all the pixels to which the damage area relates in the damage data)/(the maximum value of the gray levels of all the pixels to which the damage area relates in the damage data-the minimum value of the gray levels of all the pixels to which the damage area relates in the damage data) is between 0.8 (excluding 0.8) and 1.2 (excluding 1.2), then this historical damage data D2 is the damage data that this step needs to acquire.

Through the preset relationship, historical skin damage data which is similar to the skin damage data acquired in the S101 in area and pixel value is selected.

S302-2, obtaining the weight of the human body part obtained in S301 according to the historical skin damage data.

The weights in this step are used to characterize the likelihood of skin disorders at the body site.

Specifically, one implementation manner of this step is:

1.1 determining the skin damage area in each historical skin damage data and the gray value of each pixel point related to the skin damage.

1.2 determine the weight from all skin lesion areas and all gray values.

Since the historical lesion data is obtained, the length, width, and lesion site image of the lesion site is obtained, where the lesion area (e.g. length, width, or 3.1415926 length, width/4) and the gray value of each pixel point in the lesion site image can be obtained based on the length and width.

At this time, 1.1 the implementation scheme of determining the weight according to all the skin damage areas and all the gray values is as follows: 1) and calculating the gray index of each historical skin damage data. 2) And sequencing all historical skin damage data from front to back according to the acquisition time to obtain a sequencing sequence. 3) Starting from the first historical damage data of the sorting sequence, the area difference and the gray index difference between the first historical damage data and the first historical damage data sorted behind the first historical damage data are calculated. 4) The weight is determined as (maximum of area difference/| mean of all area differences-skin damage area | of last historical skin damage data in the sorted sequence) per (mean of skin damage area in all historical skin damage data/skin damage area in skin damage data) (| gray scale index of skin damage data-gray scale index of last historical skin damage data |/mean of gray scale index difference).

The gray level index of any skin damage data = mean value of gray levels of pixels related to skin damage in any skin damage data. Or, the gray scale index of any skin damage data = E x (ave-min)/(max-ave).

Any piece of damage data may be historical damage data, or may be damage data acquired in S101.

E is a standard deviation of gray values of each pixel point related to the skin loss in any skin loss data, ave is an average value of gray values of each pixel point related to the skin loss in any skin loss data, min is a minimum value of gray values of each pixel point related to the skin loss in any skin loss data, and max is a maximum value of gray values of each pixel point related to the skin loss in any skin loss data.

Taking the historical data as D2, D3, D4, and D5, and the gray scale index of any one of the to-be-processed data = the mean of the gray scale values of the pixels involved in the skin loss in any one of the to-be-processed data, the area of the skin loss portion in D2 (e.g., S21) and the gray scale value of each pixel are calculated, and the mean of the gray scale values of the pixels in D2 is taken as the gray scale index of D2 (e.g., G21). Similarly, the area of the skin damage portion in D3 (e.g., S31) and the gray scale value of each pixel are calculated, and the mean of the gray scale values of each pixel in D3 is used as the gray scale index of D3 (e.g., G31). The area of the skin damage part in D4 (e.g. S41) and the gray value of each pixel are calculated, and the mean value of the gray values of each pixel in D4 is used as the gray index of D4 (e.g. G41). The area of the skin damage part in D5 (e.g. S51) and the gray value of each pixel are calculated, and the mean value of the gray values of each pixel in D5 is used as the gray index of D5 (e.g. G51).

Arranging the D2, D3, D4 and D5 in the acquisition order, wherein the arrangement order is as follows: d5, D3, D2 and D4. Then the area differences S51-S31 between D5 and D3 (for convenience of description, the values of S51-S31 are denoted as DS 531), the area differences S31-S21 between D3 and D2 (for convenience of description, the values of S31-S21 are denoted as DS 321), and the area differences S21-S41 between D2 and D4 (for convenience of description, the values of S21-S41 are denoted as DS 241) are calculated. Gray scale index differences G51 to G31 between D5 and D3 (for convenience of description, values of G51 to G31 are denoted as DG 531), gray scale index differences G31 to G21 between D3 and D2 (for convenience of description, values of G31 to G21 are denoted as DG 321), and gray scale index differences G21 to G41 between D2 and D4 (values of G21 to G41 are denoted as DG241 for convenience of description) are calculated.

If the maximum value among DS531, DS321, and DS241 is DS321, the minimum value is DS241, the average value is (DS 531+ DS321+ DS 241)/3 (for convenience of description, it is denoted as ES 1), and if the maximum value among DG531, DG321, and DG241 is DG531, the minimum value is DG321, and the average value is (DG 531+ DG321+ DG 241)/3 (for convenience of description, it is denoted as EG1), the weight = (DS321/| ES1-S41|) (the average value of S21, S31, S41, S51/the area of skin loss in skin loss data) (| gray index-G51 |/EG1 of skin loss data obtained in S101).

Wherein 1) | the average value of the skin damage areas in all the historical skin damage data-the skin damage area | of the last historical skin damage data in the sorting sequence describes the area difference between the latest one historical skin damage data and the average data, 2) the average value of the skin damage areas in all the historical skin damage data/the skin damage area in the skin damage data describes the relationship between the skin damage area in the historical skin damage data and the skin damage data area of the skin damage data obtained in S101, and 3) | the gray scale index of the skin damage data-the average value of the gray scale index |/gray scale index difference of the last historical skin damage data describes the relationship between the difference between the gray scale index of the skin damage data obtained in S101 and the latest historical skin damage data index and the average difference of the gray scale index in the historical skin damage data. The similarity between the skin damage data collected in the step S101 and the historical skin damage data can be reflected by the three indexes, and the possibility of the skin damage data collected in the step S101 to be ill can be known according to the final ill condition of the historical skin damage data, so that the weight can be used for representing the possibility of skin diseases.

In addition, when the historical skin damage data is selected, the same historical data of the human body part is selected, so the weight can be used for representing the possibility of skin diseases of the human body part.

Besides the above implementation manner, the step can be implemented in the following manner, and another implementation manner of the step is as follows:

2.1 determining the skin damage area in each historical skin damage data, the gray value of each pixel point related to the skin damage and the accurate diagnosis conclusion of the skin disease.

Wherein, the diagnosis result of the skin disease is no skin disease or the name of the skin disease.

2.2 classifying the historical skin damage data corresponding to the same skin disease diagnosis conclusion into one category.

And 2.3, determining the weight corresponding to each class according to all the skin damage areas and all the gray values to form a weight vector.

Wherein, each element in the vector is a corresponding relation, and the corresponding relation is the corresponding relation between the skin disease diagnosis conclusion corresponding to the class and the weight corresponding to the class.

2.4 the weight vector is used as the weight of the human body part acquired in S301.

The implementation process of determining the weight corresponding to each class according to all the skin damage areas and all the gray values in 2.3 is as follows: and aiming at each class, 1) calculating a gray index of each historical skin damage data in the class. 2) And sequencing all historical skin damage data from front to back according to the acquisition time to obtain a sequencing sequence. 3) Starting from the first historical damage data of the sorted sequence, the area difference and the gray index difference of the first historical damage data after the first historical damage data are calculated. 4) Determining the weight corresponding to the class as (the number of historical skin damage data in the class/the total number of the historical skin damage data) × (the maximum value of the area difference/| the mean value of all the area differences-the skin damage area | of the last historical skin damage data in the sequencing sequence) (the mean value of the skin damage areas in all the historical skin damage data/the skin damage area in the skin damage data) | the gray index of the skin damage data-the gray index |/the mean value of the gray index difference of the last historical skin damage data).

Taking the historical data of a certain class as D20, D30, D40 and D50, taking the gray scale index of any to-be-processed data = the mean value of the gray scale values of the pixels involved in the skin loss in any to-be-processed data as an example, the area of the skin loss part in D20 (e.g., S22) and the gray scale value of each pixel are calculated, and the mean value of the gray scale values of the pixels in D20 is taken as the gray scale index of D20 (e.g., G22). Similarly, the area of the skin damage portion in D30 (e.g., S32) and the gray scale value of each pixel are calculated, and the mean of the gray scale values of each pixel in D30 is used as the gray scale index of D30 (e.g., G32). The area of the skin damage part in D40 (e.g. S42) and the gray value of each pixel are calculated, and the mean value of the gray values of each pixel in D40 is used as the gray index of D40 (e.g. G42). The area of the skin damage part in D50 (e.g. S52) and the gray value of each pixel are calculated, and the mean value of the gray values of each pixel in D50 is used as the gray index of D50 (e.g. G52).

Arranging the D20, D30, D40 and D50 in the acquisition order, wherein the arrangement order is as follows: d50, D30, D20 and D40. Then the area differences S52-S32 between D50 and D30 (for convenience of description, the values of S52-S32 are denoted as DS 532), the area differences S32-S22 between D30 and D20 (for convenience of description, the values of S32-S22 are denoted as DS 322), and the area differences S22-S42 between D20 and D40 (for convenience of description, the values of S22-S42 are denoted as DS 242) are calculated. Gray scale index differences G52 to G32 between D50 and D30 (for convenience of description, values of G52 to G32 are denoted as DG 532), gray scale index differences G32 to G22 between D30 and D20 (for convenience of description, values of G32 to G22 are denoted as DG 322), and gray scale index differences G22 to G42 between D20 and D40 (values of G22 to G42 are denoted as DG 242) are calculated.

If the maximum value among DS532, DS322, and DS242 is DS322, the minimum value is DS242, the average value is (DS 532+ DS322+ DS 242)/3 (for convenience of description, it is denoted as ES 2), and if the maximum value among DG532, DG322, and DG242 is DG532, the minimum value is DG322, and the average value is (DG 532+ DG322+ DG 242)/3 (for convenience of description, it is denoted as EG2), the weight = (number of historical skin damage data in the class/total number of historical skin damage data acquired in S102-2-1) (= (DS322/| ES2-S42|) (average value of S22, S32, S42, and S52/area of skin damage in skin damage data) | G52|/EG2 of skin damage data acquired in S101).

Wherein, for any class, 1) the number of historical skin damage data in the class/the total number of historical skin damage data obtained in S302-1 describes the percentage of skin damage data volume in the class; 2) i the mean value of the skin damage areas in all historical skin damage data-the skin damage area of the last historical skin damage data in the sorting sequence | describes the area difference between the latest one historical skin damage data and the mean data, 3) the mean value of the skin damage areas in all historical skin damage data/the skin damage area in the skin damage data describes the relationship between the skin damage area in the historical skin damage data and the skin damage data area of the skin damage data acquired in S101, and 4) | the gray scale index of the skin damage data-the gray scale index of the last historical skin damage data |/the mean value of the gray scale index difference describes the relationship between the difference between the gray scale index of the skin damage data acquired in S101 and the latest one historical skin damage data index and the mean value of the gray scale index in the historical skin damage data. The similarity between the skin damage data acquired in the step S101 and the historical skin damage data and the disease probability of the type can be reflected by the three indexes, and the disease probability of the skin damage data acquired in the step S101 can be known according to the final disease condition of the historical skin damage data, so that the weight can be used for representing the possibility of skin diseases corresponding to the type. In addition, when the historical skin damage data is selected, the historical data which are the same with the human body part are selected, so the weight can be used for representing the possibility that the corresponding skin diseases of the human body part are obtained.

S302-3, determining the weight as the user attribute.

And S303, the data analysis module marks the human body part and the user attribute as marked contents on the skin damage data.

And if the human body part is the denomination, marking the denomination, the user ID and the weight as marked contents on the skin damage data.

Besides the human body part and the user attribute, the label content may also include other attributes, for example, information labeled in the following three dimensions: the three dimensions are: picture type, location characteristics, skin damage characteristics.

The picture type indexes are divided into clinical general pictures, skin mirror pictures, pathological pictures, ultrasonic pictures and the like; the difference is based on different disease site characteristics and skin lesion characteristics, such as psoriasis (site characteristic 20 body site divisions, skin lesion characteristic 4 "area/erythema/infiltration/scaling"), atopic dermatitis (site characteristic 19 body site divisions, skin lesion characteristic 6 "erythema/papular edema/exudative crusting/epidermal exfoliation/lichenification/dry skin").

And during labeling, the labeling can be realized through a trained labeling model. The training process of the model can be realized by adopting any conventional artificial intelligence scheme, namely, any conventional big data self-learning method is adopted, and artificial intelligence self-learning training is carried out by inputting a large amount of sample data to form a trained marking model.

And S103, analyzing data.

The data analysis module analyzes the information stored by the data storage module.

And if the information stored in the data storage module is the labeled information, the data analysis module analyzes the labeled information stored in the data storage module.

When the data analysis module analyzes the information stored in the data storage module, or analyzes the labeled data, or analyzes the desensitized information, the data analysis module can determine a skin damage index value according to the information stored in the data storage module, and then determine an incidence relation between the skin damage index value and the skin disease. For example, the data analysis module compares the similarity degree of each skin lesion index value and the skin disease related index, finds the most similar skin disease, and further obtains an analysis result.

In addition, the data analysis module can also obtain the diagnosis standard of the skin diseases according to the skin damage data, generate a form according to the diagnosis standard, obtain feedback information based on the form, and obtain an auxiliary inquiry result according to a preset decision tree and the feedback information.

The diagnostic criteria here are as follows: symptoms (skin lesion area, skin lesion humidity, degree of dandruff, duration of days of symptoms, etc.), degree of influence (whether or not sleep is impossible, whether or not itching is present, etc.), psychological conditions (e.g., influence on mood, etc.).

The diagnostic criteria were obtained as follows: as can be seen from the description in S101, if the skin damage data is the acquired image, the features in the image, such as the area, the color, and the like, are identified by the existing image identification scheme, the suspected disease is identified based on the features, such as the area, the color, and the like, and the diagnostic standard of the suspected disease is used as the diagnostic standard here.

The specific process can be realized by a trained recognition model, for example, if a large number of sample pictures marked with symptoms are input and trained by the existing classification algorithm to obtain the trained classification model, the suspected symptoms can be output by the model.

The implementation process of the data analysis module generating the form according to the diagnosis standard is as follows:

s401, determining an acquisition index according to the diagnosis standard.

The acquisition index here is a specific acquisition target. For example: the diagnostic criteria is the degree of influence, and the acquisition indicator is the number of days that sleep fails or is unstable.

In addition, the collection index may include other attributes, such as a sequence number, which is used to describe the location of the index in the final form. Like the 1 st question in the table, the serial number is 1, but of course, it could also be 0. The initial value of the sequence number is null, but the sequence number corresponding to the 1 st question in the form and the subsequent sequence numbers can be set by themselves as long as the sequence number corresponding to the 1 st question in the form is not null and the subsequent sequence number is greater than the initial sequence number. Such as 1, 2, 3, … …, and further such as: 0. 1, 2, … …, also as: 4.5, 6, … …, or, 2, 4, 6, … …, or, 3, 4, 6, 8, … …, etc. The design of the serial number corresponding to the 1 st question in the form and the subsequent serial numbers is not limited in this embodiment.

In practice, the initial value of the serial number of each acquisition index is null. The NULL value may be NULL or NULL that is not filled, and the present embodiment does not limit the specific form of the NULL value.

In addition, this step may also determine the acquisition time based on diagnostic criteria. If the acquisition time is as follows: before, during, or after the interrogation. The acquisition time is used to describe the presentation time of the form.

S402, determining the logical relation among the acquisition indexes.

The logical relationship between any two indexes is used for describing the sequence of the appearance of any two indexes in the form.

If the logical relationship between index 1 and index 5 is: index 1 occurs in the form before index 5.

And S403, determining the position sequence of each acquisition index in the form according to the logical relationship.

The implementation process of the step is as follows:

and S403-1, forming a set A by all the acquisition indexes, and generating an empty set B.

S403-2, selecting any acquisition index from the set A, using the acquisition index as a current processing index, and marking the serial number of the current processing index as 0.

For example, the collection index a is taken from the set a, then a is the current processing index, and the sequence number of a is changed from null value to 0.

S403-3, deleting the current processing index from the set A, and adding the current processing index into the set B.

To this end, there is no a for the elements in set a and one element, a, in set B.

That is, the elements in set A are collection indices for which no position in the form is determined. The elements in set B are collection indices for which the position in the form has been determined.

S403-4, determining whether the first index exists.

Wherein the first indicator is an element in the set a, and the logical relationship between the first indicator and the current processing indicator is: the first indicator appears in the form prior to the current processing indicator.

The first index is the index with the position prior to a and the serial number is null. That is, the first index is that the current position has not yet determined its position in the form, and its position must occur prior to a.

S403-5, if the first index exists, determining a second index corresponding to the first index. And marking the serial number of the first index as X-1, wherein X is the minimum serial number in the second index. And deleting the first index from the set A, and adding the first index into the set B.

Wherein, the second index is an element in the set B, and the logical relationship between the second index and the first index is: the second indicator appears in the form after the first indicator.

If the first index is the acquisition index b, the position marked in the step is found, and the position appears in the acquisition index after b. For all the collected indexes marked with positions and positioned after b, the smallest sequence number (namely the most front position) is selected, the position of b is determined as the sequence number before the smallest sequence number, for example, the smallest sequence number is 6, then the sequence number of b is 5, and therefore, b is ensured to be before all the collected indexes behind the b.

And S403-6, determining whether the third index exists.

Wherein the third indicator is an element in the set a, and the logical relationship between the third indicator and the current processing indicator is: the third indicator appears in the form after the current processing indicator.

In this step, the index with the position a after the position is searched and the serial number is null, i.e. the third index. That is, the third indicator is that its position in the form has not been determined so far, and its position appears after a.

And S403-7, if the third index exists, determining a fourth index corresponding to the third index. And marking the serial number of the third index as Y +1, wherein Y is the largest serial number in the fourth index. And deleting the third index from the set A, and adding the third index into the set B.

Wherein, the fourth indicator is an element in the set B, and the logical relationship between the fourth indicator and the third indicator is: the fourth metric occurs in the form before the third metric.

If the third index is the collection index c, the collection index with the marked position and the position before c is found in the step. For all the collected indexes marked with positions and positioned before c, the largest sequence number (namely, the most rear position) is selected, the position of c is determined as the sequence number after the largest sequence number, for example, the largest sequence number is 9, then the sequence number of c is 10, and thus, c is ensured to be behind all the collected indexes before the position of c.

In this way, the collection indicators that have a logical relationship with the current processing indicator each determine their position in the form.

And S403-8, if the set A is not an empty set, taking any acquisition index from the set A as a current processing index, and repeatedly executing the steps S102-3-3 to S102-3-7 until the set A is an empty set, or each element in the set A does not have the first index and the third index.

If the set A is not an empty set, that is to say there are also acquisition indicators for which no position has been determined. There are two possible situations when the set a is not empty, one is that it has not yet turned to its confirmed position, and the other is that it has no collection index with logical relationship, e.g., the index can be placed at any position of the form, and there is no definite position in sequence. For example, the degree of influence of eczema on the user is only caused by the presence of eczema symptoms, and the acquisition indicator "the degree of eczema symptoms (none, mild, moderate, severe, very severe)" precedes the acquisition indicator "how uncomfortable the eczema gives you a sense of incongruity". For another example, there is no obvious precedence relationship between the collection index "degree of eczema symptom (none, mild, moderate, severe, very severe)" and the collection index "current working pressure (none, mild, general, large, very large)", so there is no logical relationship between the collection index "current working pressure (none, mild, general, large, very large)" and the collection index "degree of eczema symptom (none, mild, moderate, severe, very severe)", and the collection index "current working pressure (none, mild, general, large, very large)" is an index placed at any position of the form, and the index will exist in the set a, but the collection index does not have the first index and the third index.

Then, when the set a is empty or no first index or third index exists in any element in the set a, it indicates that the logical relationship exists in all the collected indexes and the positions are determined, and at this time, the loop ends and goes to step S403-9.

In addition, if the set a is not empty, the serial numbers of all the elements in the set a may be labeled as S, or other characters may be used, as long as the serial numbers are clearly distinguished from the labels of the collection indexes having a logical relationship. That is, for the collected index in which the first index and the third index do not exist, the index thereof is labeled as S or the like.

And S403-9, determining the position sequence of each acquisition index in the form according to the sequence number of the acquisition index.

The implementation of the step is related to whether the set A is an empty set, if the set A is empty, the position of all the acquisition indexes is specified, and if the set A is not empty, the position of a part of the acquisition indexes can be placed at any position in the form and has no fixed position.

Specifically, if the set a is an empty set, the implementation process of S403-9 is as follows:

4.1 determine the minimum value min of the sequence numbers in all the elements of set B.

4.2, determining whether the same-sequence-number elements exist in the set B, if the same-sequence-number elements do not exist in the set B, executing the step 4.3, and if the same-sequence-number elements exist in the set B, executing the step 4.4 and the step 4.5.

4.3 the position of each acquisition index in the form is the serial number-min +1 of each acquisition index.

The set B does not have elements with the same serial number, and the position of each acquisition index is unique, so that the position of each acquisition index in the form is the serial number-min +1 of each acquisition index.

4.4, determining the original value of each acquisition index as the serial number-min +1 of each acquisition index.

If the elements with the same sequence number exist in the set B, the acquisition indexes with the same position exist, the sequence number-min +1 of the acquisition index cannot be used as the final position at the moment, and the same sequence number needs to be adjusted, so that the sequence number-min +1 of the acquisition index can only be used as the original value of the acquisition index, and the final position can be obtained after the original value is adjusted. The adjustment process is as follows:

1) and sequencing the acquisition indexes in the order of small to large original values. 2) From the first element of the sequence, it is confirmed whether the same original value exists or not, successively. If the same original value does not exist, the processing is not carried out, the next element is continuously confirmed backwards, if the same original value exists, the number n of the same original value is determined (1), the same original value is adjusted according to n, and the original value of the collected index after the same original value is updated to be the original value + n-1 before updating. (2) And starting from the original value after the same original value, re-executing the steps of sequentially confirming whether the same original value exists or not, if so, determining the number n of the same original value, adjusting the same original value according to n, and updating the original value of the index collected after the same original value into the original value before updating + n-1 until all the original values are confirmed.

That is, it is confirmed whether the same original value exists or not, sequentially from the first element of the sequence. If the same original value does not exist, no processing is carried out, the next element is continuously confirmed backwards, if the same original value exists, the same original value is adjusted, all original values behind the same original value are adjusted backwards at the same time, then whether the same original value exists or not is confirmed backwards and forwards again in sequence from the original value behind the same original value until all the original values are confirmed, the original values at the moment are different, and then the current original value is used as the position of each acquisition index in the form.

The process of adjusting the same original value, that is, the specific implementation process of adjusting the same original value according to n, is as follows:

5.1, counting in the historical forms, and simultaneously presenting the number m1 of the forms of all indexes to be adjusted and the sequence of the indexes to be adjusted in each form.

The index to be adjusted is an acquisition index corresponding to the same original value.

For example, there are 4 identical original values, and the corresponding acquisition indicators are the indicators d1, d2, d3, and d 4. Then n =4, 2.1 looks up the number of forms in which d1, d2, d3 and d4 occur simultaneously, i.e. m1, and the order of occurrence of d1, d2, d3 and d4 in each form in the history form.

5.2 for any index i to be adjusted, counting in a history table, counting the number m2i of the table with the index i to be adjusted, calculating the adjustment coefficient wi of the index i to be adjusted,

wherein wi = (m1/m2i) × SQRT (S/m1) + Z,

S=POWER[(a1i-m1)/2]+POWER[(a2i-m1)/2]+…+POWER[(ani-m1)/2]

SQRT () is an open root function, POWER () is a square function, a1i is the number of forms in which all indexes to be adjusted appear simultaneously, and the index i to be adjusted precedes all other indexes to be adjusted; a2i is the number of the forms in which the index i to be adjusted is located at the second place of all other indexes to be adjusted in all the forms in which the indexes to be adjusted appear at the same time; ani is the number of the forms in which all the indexes to be adjusted appear simultaneously, the indexes to be adjusted i finally appear in all other indexes to be adjusted, and Z is a random decimal.

For example, for d1, its adjustment coefficient w1= (m1/m21) × SQRT (S/m1) + Z,

S=POWER[(a11-m1)/2]+POWER[(a21-m1)/2]+POWER[(a31-m1)/2]+POWER[(a41-m1)/2]+Z。

wherein m21 is the total number of forms with d1 appearing in all history forms, a11 is the number of forms with d1, d2, d3 and d4 appearing at the same time, and d1 appears before d2, d3 and d4 (namely d1 appears first). In the case of a21 being the number of forms in which d1, d2, d3 and d4 occur simultaneously, d1 is located at the second position (e.g. d2 appears first, then d1 appears first, d3 and d4 appear last, or d3 appears first, then d1 appears last, d2 and d4 appear last, or d4 appears first, then d1 appears last, and d2 and d3 appear last, note that the two indexes to be adjusted appearing last in the above case are not limited, and take "d 3 and d4 appear last" as an example to indicate the number of forms in which the last appearance sequence may be d3, then d4, or d4, then d 3). a31 is the number of forms in which d1, d2, d3 and d4 occur simultaneously, d1 is located in the third place (e.g., d2 and d3 appear first, then d1 appears last, and d4 appears last, or d2 and d4 appear first, then d1 appears last, and d3 appears last, or d3 and d4 appear first, then d1 appears last, and d2 appears last, note that the order of the two indexes to be adjusted appearing first in the above case is not limited, and taking "d 2 and d3 appear first" as an example, it means that the first appearance order may be d2, then d3, or d3, then d 1). a41 is the number of forms that appear at the same time as d1, d2, d3 and d4, and d1 is located at the fourth position, i.e., d1 is the number of forms that appear after d2, d3 and d4 (i.e., d1 appears last).

Is a randomly generated decimal.

5.3, sorting the indexes to be adjusted according to the adjustment coefficients from large to small, and determining the sorting label b of each adjustment index.

Wherein the ranking index of the index to be adjusted ranked at the first position is 0.

For example, if the sequences obtained after sorting based on the adjustment coefficients of d1, d2, d3, and d4 are d4, d2, d3, and d1, the sorting number of d4 is 0, the sorting number of d2 is 1, the sorting number of d3 is 2, and the sorting number of d1 is 4.

It is possible that the values of the two indexes to be adjusted (m1/m21) × SQRT (S/m1) are the same, and if the sorting is performed based on the results of (m1/m21) × SQRT (S/m1), a parallel situation may occur, and at this time, Z in the formula can effectively avoid the parallel situation, and it is ensured that the adjustment coefficients of each index to be adjusted are different.

5.4 adjusting the original value of each index to be adjusted to the original value + b before adjustment.

And 5.5, determining the current original value of each acquisition index as the position of each acquisition index in the form.

(II) if the set A is not an empty set, the implementation process of S102-3-9 is as follows:

6.1 determining the number e1 of elements in the set A, and determining the total number e2 of the acquisition indexes.

The elements in set A at this point are collection metrics that can be put anywhere in the form.

6.2 randomly selecting e1 numbers from continuous positive integers from 1 to e2, and randomly allocating the numbers to each element in the set A as the positions of the corresponding acquisition indexes in the form.

Executing the steps, firstly determining the positions of the elements in the set A in the form, and then only inserting the acquisition indexes with definite position relations into the form. See step 6.3-step 6.10.

6.3 determine the minimum value min of the sequence numbers in all the elements of set B.

6.4 determine if there are elements in set B that have the same sequence number. If the same element number does not exist in the set B, step 6.5 to step 6.7 are executed, and if the same element number exists in the set B, step 6.8 and step 6.10 are executed.

6.5, determining the first value of the acquisition index corresponding to each element in the set B as the serial number-min +1 of the acquisition index.

6.6 sorting the corresponding acquisition indexes from small to large according to the first value.

6.7 selecting one acquisition index in turn from the first acquisition index in the sequence, and if the first value of the selected acquisition index is not one of the randomly selected e1 numbers, using the first value as the position of the selected acquisition index in the form. And if the first value of the selected acquisition index is one of the randomly selected e1, taking the first value +1 as the position of the selected acquisition index in the form, and updating the first values of all the acquisition indexes after the selected acquisition index to the first value +1 before updating.

The set B does not have the elements with the same serial number, which indicates that the position of each acquisition index is unique, so the serial number-min +1 of the acquisition index is first used as the first value of the acquisition index, and if the first value is the same as the position allocated to each element in the set a in step 6.2, it indicates that the positions of the two acquisition indexes are the same, and the position of the index in the set B is adjusted.

6.8, determining the second value of each acquisition index as the serial number-min +1 of each acquisition index.

6.9 sequencing the acquisition indexes in the order from small to large according to the second value.

6.10 selecting one acquisition index in turn starting from the second acquisition index,

s501, if the second value of the selected acquisition index is unique, if the second value of the selected acquisition index is not one of the randomly selected e1 numbers, the second value is used as the position of the selected acquisition index in the form. And when the second value of the selected acquisition index is one of the randomly selected e1, taking the second value +1 as the position of the selected acquisition index in the form, and updating the second values of all the acquisition indexes after the selected acquisition index to the second value +1 before updating.

S502, if the second value of the selected acquisition index is not unique, marking an acquisition index before the selected acquisition index as an initial index, determining the same second value number n, adjusting the same second value according to n, and updating the second value of the acquisition index after the same second value to be the current second value + n-1. And sequencing the acquisition indexes in the sequence from small to large according to the updated second value, sequentially selecting one acquisition index from the next acquisition index of the initial indexes in the sequence, and repeatedly executing the step S501 and the step S502 until all the acquisition indexes have the positions of the acquisition indexes in the form.

The specific implementation process of adjusting the same original value according to n is as follows:

and counting in the historical forms, wherein the number m1 of the forms of all indexes to be adjusted simultaneously appears, and the sequence of the indexes to be adjusted in each form is counted. The index to be adjusted is the acquisition index corresponding to the same original value.

For any index i to be adjusted, counting in a history table, wherein the number m2i of the table with the index i to be adjusted appears, calculating an adjustment coefficient wi of the index i to be adjusted,

wherein wi = (m1/m2i) × SQRT (S/m1) + Z,

S=POWER[(a1i-m1)/2]+POWER[(a2i-m1)/2]+…+POWER[(ani-m1)/2]

And sorting the indexes to be adjusted according to the adjustment coefficients from large to small, and determining a sorting label b of each adjustment index, wherein the sorting label of the index to be adjusted arranged at the first position is 0.

And adjusting the original value of each index to be adjusted to be the original value + b before adjustment.

And S404, generating a form for each acquisition index according to the position sequence.

And generating the acquisition indexes into a form according to the position sequence of each acquisition index.

For example, the collection index is a form obtained through a decision tree data model.

The data presentation module also presents the form. After the form is displayed, the patient fills in the form and obtains the filled-in information (i.e., feedback information). And the data analysis module acquires feedback information based on the form and then obtains an auxiliary inquiry result according to a preset decision tree and the feedback information. Wherein the decision tree is implemented by existing schemes.

Specifically, an inquiry curve is obtained according to a preset decision tree and feedback information. And historical feedback information related to the feedback information is obtained, and a historical inquiry curve is formed according to the historical feedback information. And comparing the historical inquiry curve with the inquiry curve to obtain an auxiliary inquiry result.

In addition, in the process of obtaining a report or an inquiry curve, scoring is performed based on the feedback information, and the scoring standard is not limited in this embodiment.

The form in the embodiment is formed by analysis and combination in the system, the specific content of the indexes in the form and the front-back relationship among the indexes are dynamically generated, so that the form which accords with the symptoms of each patient can be provided for each patient, thousands of people and thousands of faces of the form are realized, the skin disease characteristics obtained through the form can more accurately and comprehensively reflect the symptoms of the current user, and the defect that a doctor inquires that the patient has one-sided and empirical types is overcome.

And S104, displaying the analysis result.

And the data display module displays the analysis result, the auxiliary inquiry result, the form and the like obtained by the data analysis module.

Wherein, when the form is displayed, the form can be displayed based on the acquisition time. If the acquisition time is before the inquiry, the form can be displayed when the user enters, if the acquisition time is in the inquiry, the form can be displayed in the inquiry process of the user, and if the acquisition time is after the inquiry, the form can be displayed after the inquiry of the user.

When the analysis result and the auxiliary inquiry result are displayed, a curve form can be adopted, and besides, a report form can also be adopted.

The remote auxiliary medical treatment is realized through the data acquisition module, the data storage module, the data analysis module and the data display module. In addition, the skin disease information acquired by the system is acquired in a real-time view mode, the mode is suitable for any acquisition equipment, the application scene of disease course management is expanded, and the disease course management aiming at the universal environment is realized.

Next, the whole process of the user a actually applying the system for managing the course of skin diseases provided by this embodiment is taken as an example, and the implementation process of the system provided by this embodiment in an actual scene is described again.

If the course management system for skin diseases provided by this embodiment is provided for the user to call in the form of APP, then

1. User A opens APP

2. The APP executes the diagnosis according to the disease course management system of the skin disease.

In particular, the method comprises the following steps of,

1) the course management system for skin diseases executes step S101 to collect data of the user a.

2) The course management system for skin diseases executes step S102, and stores the acquired data.

3) The course management system for skin diseases executes step S103, determines a diagnosis standard from the collected skin lesion data, generates a form based on the diagnosis standard, and presents the form to the user a. After the user a fills in the form, the course management system for skin diseases acquires the filling content, and performs analysis according to the filling content and the data acquired in S101 to obtain an analysis result.

4) The course management system for skin diseases executes step S104, and presents the analysis result to the user a.

The user A not only can diagnose the state of an illness through the system acquisition information provided by the embodiment at any time and any place, but also can more accurately reflect the state of an illness through a form suitable for the user, and more accurate diagnosis results are obtained.

In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Various modifications and alterations of this invention may be made by those skilled in the art without departing from the spirit and scope of this invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.

Claims

1. A system for managing the course of a skin disorder, the system comprising: the data acquisition module, the data storage module, the data analysis module and the data display module;

if the boundary line is a closed curve, analyzing according to the relation between the acquisition auxiliary line and the skin damage area;

the data analysis module is used for acquiring a diagnosis standard of skin diseases according to the skin damage data, generating a form according to the diagnosis standard, acquiring feedback information based on the form, and acquiring an auxiliary inquiry result according to a preset decision tree and the feedback information;

the data display module is used for displaying the form and the auxiliary inquiry result;

the data analysis module generates a form according to the diagnosis standard, and specifically includes:

determining an acquisition index according to the diagnosis standard; the acquisition index comprises a serial number; the initial value of the serial number is a null value;

determining a logical relation among all the acquisition indexes; the logical relation between any two indexes is used for describing the sequence of the occurrence of the any two indexes in the form;

determining the position sequence of each acquisition index in the form according to the sequence number and the logical relationship;

and generating a form for each acquisition index according to the position sequence.

2. The system of claim 1, wherein the data analysis module is further configured to determine a current collection object and a collection standard according to the diagnosis standard, and generate a collection description according to the current collection object and the collection standard; starting the data acquisition module, analyzing the real-time framing of the data acquisition module, and controlling the data acquisition module to acquire a current image to obtain skin damage data if the analysis result meets the acquisition standard;

and the data display module is used for displaying the acquisition instruction.

3. The system according to claim 2, wherein the acquisition auxiliary line is a closed curve;

the analyzing according to the relation between the collection auxiliary line and the skin damage area comprises the following steps:

forming a standard set by pixel points in the region surrounded by the acquisition auxiliary lines;

forming a skin damage set by the pixel points related to the skin damage area;

obtaining an intersection of the first set which is a standard set and a skin damage set;

if the elements in the element/standard set in the first set are <0.8 x (1+ total pixels of the image acquired by the element/data acquisition module in the standard set), obtaining an analysis result which does not meet the acquisition standard;

if the element in the element/standard set in the first set is 0.8 (1+ total pixels of the image acquired by the element/data acquisition module in the standard set), obtaining a second set as a skin loss set-standard set, and if the second set is an empty set, obtaining an analysis result meeting the acquisition standard; and if the second set is not an empty set, obtaining an analysis result according to the second set.

4. The system of claim 3, wherein said deriving analysis results from said second set comprises:

determining the number of continuous areas corresponding to the elements in the second set and the number of elements in the second set included in each continuous area;

if the number of elements included in the maximum continuous area/the total number of elements in the first set is greater than 0.5, or the number of the continuous areas is greater than 4, obtaining an analysis result which does not accord with the acquisition standard; wherein the largest continuous area is a continuous area including the largest number of elements;

and if the number of the elements included in the maximum continuous area/the total number of the elements in the first set is less than 0.5, and the number of the continuous areas is less than 4, obtaining an analysis result meeting the acquisition standard.

5. The system according to claim 1, wherein the determining a position order of each acquisition indicator in the form according to the sequence number and the logical relationship specifically includes:

step 1, forming a set A of all acquisition indexes and generating an empty set B;

step 2, arbitrarily selecting one acquisition index from the set A, using the acquisition index as a current processing index, and marking the serial number of the current processing index as 0;

step 3, deleting the current processing index from the set A, and adding the current processing index into a set B;

step 4, determining whether a first indicator exists, wherein the first indicator is an element in the set A, and the logical relationship between the first indicator and the current processing indicator is as follows: the first index occurs in the form prior to the current processing index;

step 5, if a first index exists, determining a second index corresponding to the first index, where the second index is an element in the set B, and a logical relationship between the second index and the first index is: the second indicator appears in the form after the first indicator; marking the serial number of the first index as X-1, wherein X is the minimum serial number in the second index; deleting the first index from the set A, and adding the first index into the set B;

step 6, determining whether a third indicator exists, wherein the third indicator is an element in the set A, and the logical relationship between the third indicator and the current processing indicator is as follows: the third index appears in the form after the current processing index;

step 7, if a third index exists, determining a fourth index corresponding to the third index, where the fourth index is an element in the set B, and a logical relationship between the fourth index and the third index is: the fourth index appears in the form before the third index; marking the serial number of the third index as Y +1, wherein Y is the largest serial number in the fourth index; deleting the third index from the set A, and adding the third index into the set B;

step 8, if the set A is not an empty set, taking any acquisition index from the set A as a current processing index, and repeatedly executing the steps 3 to 7 until the set A is an empty set, or each element in the set A does not have a first index and a third index;

and 9, determining the position sequence of each acquisition index in the form according to the sequence number of the acquisition index.

6. The system according to claim 5, wherein if the set a is an empty set, the step 9 specifically includes:

determining the minimum value min of the sequence numbers in all the elements of the set B;

determining whether elements with the same sequence number exist in the set B;

if the elements with the same serial number do not exist in the set B, the position of each acquisition index in the form is the serial number-min +1 of each acquisition index;

if the elements with the same sequence number exist in the set B, determining the original value of each acquisition index as the sequence number-min +1 of each acquisition index; sequencing all the acquisition indexes in the order of small to large original values; from the first element of the sequence, whether the same original value exists is confirmed in sequence; if the same original value exists, determining the number n of the same original value, adjusting the same original value according to n, and updating the original value of the index collected after the same original value to be the original value + n-1 before updating; starting from the original value after the same original value, re-executing the steps of sequentially confirming whether the same original value exists or not, if so, determining the number n of the same original value, adjusting the same original value according to n, and updating the original value of the index collected after the same original value to the original value before updating + n-1 until all the original values are confirmed; and determining the current original value of each acquisition index as the position of each acquisition index in the form.

7. The system according to claim 5, wherein if the set a is not an empty set, the step 9 specifically comprises:

determining the number e1 of elements in the set A, and determining the total number e2 of the collection indexes;

randomly selecting e1 numbers from continuous positive integers from 1 to e2, and randomly allocating the numbers to each element in the set A as the positions of the corresponding acquisition indexes in the form;

determining whether elements with the same sequence number exist in the set B;

if the elements with the same sequence number do not exist in the set B, determining a first value of the acquisition index corresponding to each element in the set B as a sequence number-min +1 of the acquisition index; sorting the corresponding acquisition indexes from small to large according to the first value; sequentially selecting one acquisition index from the first ordered acquisition index, and if the first value of the selected acquisition index is not one of the randomly selected e1 numbers, taking the first value as the position of the selected acquisition index in the form; if the first value of the selected acquisition index is one of the randomly selected e1 numbers, taking the first value +1 as the position of the selected acquisition index in the form, and updating the first values of all the acquisition indexes after the selected acquisition index to the first value +1 before updating;

if the elements with the same sequence number exist in the set B, determining the second value of each acquisition index as the sequence number-min +1 of each acquisition index; sequencing the acquisition indexes in a sequence from small to large according to a second value; sequentially selecting one acquisition index from the second ordered acquisition index, and if the second value of the selected acquisition index is unique, taking the second value as the position of the selected acquisition index in the form when the second value of the selected acquisition index is not one of the randomly selected e1 numbers; when the second value of the selected acquisition index is one of the randomly selected e1, taking the second value +1 as the position of the selected acquisition index in the form, and updating the second values of all the acquisition indexes after the selected acquisition index to the second value +1 before updating; s902, if the second value of the selected acquisition index is not unique, marking an acquisition index before the selected acquisition index as an initial index, determining the same second value number n, adjusting the same second value according to n, and updating the second value of the acquisition index after the same second value to be the current second value + n-1; and sequencing the acquisition indexes in the sequence from small to large according to the updated second value, sequentially selecting one acquisition index from the next acquisition index of the initial index in the sequence, and repeatedly executing the step S901 and the step S902 until all the acquisition indexes have the positions of the acquisition indexes in the form.

8. The system of claim 6, wherein said adjusting the same original value according to n comprises:

counting in the historical forms, wherein the number m1 of the forms of all indexes to be adjusted and the sequence of the indexes to be adjusted in each form appear simultaneously; the indexes to be adjusted are acquisition indexes corresponding to the same original values;

counting the number m2i of the forms with the index i to be adjusted in the historical form for any index i to be adjusted, and calculating an adjustment coefficient wi of the index i to be adjusted, which is (m1/m2i) SQRT (S/m1) + Z; wherein the content of the first and second substances,

S＝POWER[(a1i-m1)/2]+POWER[(a2i-m1)/2]+…+POWER[(ani-m1)/2]；

SQRT () is an open root function, POWER () is a square function, a1i is the number of forms in which all indexes to be adjusted appear simultaneously, and the index i to be adjusted precedes all other indexes to be adjusted; a2i is the number of the forms in which the index i to be adjusted is located at the second place of all other indexes to be adjusted in all the forms in which the indexes to be adjusted appear at the same time; ani is the number of the forms in which all indexes to be adjusted appear simultaneously, the indexes to be adjusted i finally appear in all other indexes to be adjusted, and Z is a random decimal;

sorting the indexes to be adjusted according to the adjustment coefficients from large to small, and determining a sorting label b of each adjustment index, wherein the sorting label of the index to be adjusted arranged at the first position is 0;